Abstract CN10-01: Tumor surveillance strategies in pediatric hereditary cancer predisposition syndromes
Bibliographic record
Abstract
Abstract Cancer is the most common cause of disease-related death in children beyond the neonatal period. Primary prevention has not been commonly considered in the management of childhood cancers as ‘environmental’ agents have not generally been associated with cancer risk. Evidence from genetic and genomic studies supports a rapidly increasing role for inherited or de novo genetic alterations in the etiology of many childhood cancers. Diverse phenotypes have been associated with many of these gene alterations, and genotype:phenotype correlations continue to be defined. Some cancer predisposition syndromes are associated with other non-neoplastic congenital or developmental anomalies, whereas other syndromes appear to be limited to occurrence of a few or many possible cancer types. In the past, the role of genetic testing of children was questioned based on the lack of evidence for effective intervention or improved outcomes. However, the creation and implementation of several comprehensive multi-modality surveillance protocols has offered means by which children at genetic risk of cancer may benefit from early tumor detection and treatment to reduce morbidity and mortality. Emerging discussion of potential opportunities for targeted chemoprevention have also begun. This talk will highlight some of these scenarios, present algorithms whereby such approaches can be used, and explore the complex medical, psychosocial, ethical and financial challenges and opportunities whereby biological/genetic understanding of childhood cancer risk, coupled with effective clinical surveillance can beneficially alter the natural course of hereditary cancer in children. Citation Format: David Malkin, Anita Villani, Jonathan Wasserman. Tumor surveillance strategies in pediatric hereditary cancer predisposition syndromes. [abstract]. In: Proceedings of the Thirteenth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2014 Sep 27-Oct 1; New Orleans, LA. Philadelphia (PA): AACR; Can Prev Res 2015;8(10 Suppl): Abstract nr CN10-01.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".